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Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea

Authors
 Eu Jeong Ku  ;  Chaelin Lee  ;  Jaeyoon Shim  ;  Sihoon Lee  ;  Kyoung-Ah Kim  ;  Sang Wan Kim  ;  Yumie Rhee  ;  Hyo-Jeong Kim  ;  Jung Soo Lim  ;  Choon Hee Chung  ;  Sung Wan Chun  ;  Soon-Jib Yoo  ;  Ohk-Hyun Ryu  ;  Ho Chan Cho  ;  A Ram Hong  ;  Chang Ho Ahn  ;  Jung Hee Kim  ;  Man Ho Choi 
Citation
 Endocrinology and Metabolism(대한내분비학회지), Vol.36(5) : 1131-1141, 2021-10 
Journal Title
Endocrinology and Metabolism(대한내분비학회지)
ISSN
 2093-596X 
Issue Date
2021-10
Keywords
Adrenal neoplasms ; Cushing syndrome ; Primary hyperaldosteronism ; Steroid metabolism ; Supervised machine learning
Abstract
Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids.

Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing's syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors.

Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT.

Conclusion: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.
Files in This Item:
T202125042.pdf Download
DOI
10.3803/EnM.2021.1149
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Rhee, Yumie(이유미) ORCID logo https://orcid.org/0000-0003-4227-5638
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/187668
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